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Record W2151397509 · doi:10.1109/pedg.2014.6878683

Time-optimal switching surface for photovoltaic MPPT

2014· article· en· W2151397509 on OpenAlexafffund
Francisco Paz, Rafael Peña‐Alzola, Martin Ordonez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsOvershoot (microwave communication)Maximum power point trackingConvertersPhotovoltaic systemController (irrigation)Computer scienceControl theory (sociology)Power (physics)MinificationMaximum power principleTopology (electrical circuits)EngineeringElectrical engineeringPhysicsTelecommunicationsControl (management)

Abstract

fetched live from OpenAlex

The dynamic response of photovoltaic (PV) power converters plays a critical role to perform fast Maximum Power Point Tracking (MPPT). Among different options, linear controllers are a very popular choice to control power converters due to their design simplicity and basic implementation. However, the simplicity of the controller comes with a sacrifice in the speed and performance given the extremely large operating range in PV applications (V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OC</sub> to I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SC</sub> ). For example, in the boost topology, the Right-Half-Plane Zero induces overshoot and the severe inability to handle extreme operating points. In this paper, a non-linear controller based on phase-plane analysis is implemented for a boost converter for PV-battery charging applications. The novel proposed controller provides a number of advantages, including: 1) Faster transient response, close to the physical limit; 2) Overshoot elimination; 3) Minimization and tight handling of disturbances caused by changes in environmental conditions. The combination of these benefits translates into faster MPPT algorithms and a minimization of the losses during the MPPT scan process. A comparative analysis is presented to demonstrate the characteristic features of the controller, showing significant improvements over the traditional dual-loop linear controller tuned by an optimal method.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.685
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.237
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2014
Admission routes2
Has abstractyes

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